Traffic Flow Prediction System

Skills, Tech Stack, and Libraries

1. Skills: Time-Series Forecasting, Feature Engineering, Predictive Modeling, Data Visualization

2. Tech Stack: Python, SQL

3. Libraries: Pandas, NumPy, Scikit-learn, TensorFlow, Keras, Matplotlib, Seaborn

Approach

Objective:

I developed a predictive system to forecast traffic flow patterns using historical traffic data. The project aimed to assist urban planners and transportation authorities in managing congestion and optimizing traffic operations.

Approach:

1. Data Collection and Preprocessing:

   • Collected traffic flow data from public APIs and IoT-enabled sensors.

   • Cleaned the data using Pandas to handle missing values, remove outliers, and standardize timestamps.

2. Exploratory Data Analysis (EDA):

   • Conducted EDA to uncover patterns in traffic flow, identifying peak hours, seasonal trends, and anomalies.

   • Visualized trends using Matplotlib and Seaborn to determine the periodic nature of the data.

3. Feature Engineering:

   • Created lag-based features such as previous hour traffic and rolling averages to capture temporal dependencies.

   • Extracted features like day of the week, hour of the day, and weather conditions to enhance model accuracy.

4. Model Development:

   • Trained multiple models for time-series forecasting, including:

     • ARIMA: For short-term prediction and baseline comparison.

     • LSTM (Long Short-Term Memory): For capturing long-term dependencies in the traffic data.

   • Evaluated models based on metrics like Mean Absolute Error (MAE) and Mean Squared Error (MSE).

5. Prediction and Visualization:

   • Used the best-performing model to predict future traffic flow and visualize results.

   • Built a dashboard for stakeholders to monitor traffic predictions in real-time and plan accordingly.

6. Automation:

   • Automated data ingestion, model retraining, and prediction updates to ensure continuous accuracy and relevance.

     
     

Code Flow:

1. Load historical traffic data using Pandas and preprocess it by handling missing values and formatting timestamps.

2. Perform EDA to analyze trends and periodicities.

3. Engineer temporal and categorical features for model inputs.

4. Train ARIMA and LSTM models, tuning hyperparameters for optimal performance.

5. Predict traffic flow using the trained model and visualize predictions with Matplotlib.

Results

The Traffic Flow Prediction System delivered impactful results, including:

• Accurate Predictions: Achieved a reduction in prediction error with an MSE of less than 10% for both short- and long-term forecasts.

• Congestion Management: Enabled transportation authorities to identify and mitigate peak congestion times effectively.

• Scalable Solution: Supported integration with real-time data streams, enhancing its utility for dynamic traffic management.

• Operational Insights: Provided actionable insights for urban planners to design traffic control strategies, reducing congestion by up to 15%.

This project demonstrated the application of advanced time-series forecasting techniques in solving real-world transportation challenges.

Git Link

For more information and code, visit the Git link.